We investigate the use of the popular nonparametric
integrated squared error criterion in
parametric estimation. Of particular
interest are the problems of fitting normal mixture
densities and linear regression. We discuss some
theoretical properties and comparisons to maximum likelihood.
The robustness of the procedure is demonstrated by example.
The criterion may be applied in a wide range of models.
Two case studies are given: an application to a series of
yearly household income samples as well as a more complex
application involves estimating an economic frontier function
of U.S. banks where the data are assumed to be noisy.
Extensions to clustering and discrimination problems follow.
Questions? Jiming Jiang